1. Field of the Invention
The invention relates in general to a sphere decoding method applied to a multi-input multi-output (MIMO) channel, and more particularly to a sphere decoding method applied to a MIMO channel capable of reducing system complexity.
2. Description of the Related Art
With the development of the wireless communication technology, standards of wireless local area network have evolved from 802.11a/b/g standard for single-input single-output (SISO) mode into 802.11n standard for multi-input multi-output (MIMO) mode, so as to meet the requirement of higher speed data transmission. The MIMO technology utilizes multiple transmission antennas and reception antennas for respectively transmitting and receiving signals. As compared with the conventional SISO system, the MIMO system can provide multiple parallel data streams on the same frequency band at the same time, thereby increasing the data transmission amount by multiples.
The MIMO system has to effectively use an equivalent channel with orthogonal characteristics in space to transmit the multiple parallel data streams on the same frequency band. However, the equivalent channel with orthogonal characteristics in space decays to different extents depending on geometric arrangement and element characteristic of the transmission antennas and reception antennas and geometric and statistic features of the transmission paths. Therefore, signal detection becomes an important issue in the MIMO system design. In general, the complexity of a signal detection system is raised for reducing the error rate of signal reception.
In present signal detection methods, the maximum likelihood (ML) rule can provide optimal receiving performance, but increase the complexity of the signal detection system to a too-high extent, causing difficulties in hardware implementation. Therefore, a sphere decoding method is developed to simultaneously have a receiving performance close to that of the maximum likelihood rule and suitable system complexity. In the sphere decoding method, a K-Best breadth-first sphere decoding method is very suitable for hardware implementation because of its fixed throughput and fixed complexity.
The receiving performance of the K-Best sphere decoding method is determined by a value of K. If the value of K gets larger, the signal receiving performance becomes higher, but the system complexity is increased. If the value of K gets smaller, the system complexity is decreased, but the signal receiving performance becomes lower. Therefore, in the K-Best sphere decoding method, the value of K has to be determined to meet the requirements of system complexity and receiving performance. In addition, in the K-Best sphere decoding method, each detection layer includes M constellation points, wherein M is a positive integer. In the K-best sphere decoding method, each detection layer receives K preferred points from the previous detection layer, and K×M partial Euclidean distances have to be calculated and then sorted to obtain K preferred points of the present detection layer to transfer to the next detection layer.
If T signals are transmitted via the MIMO channel, the K-best sphere decoding method corresponds to T detection layer in the complex domain, wherein T is a positive integer. Thus, the K-best sphere decoding method has to calculate the partial Euclidean distances M+(T−2)×K×M+K times and perform a sorting operation once every detection layer. However, as for a high-order MIMO system, when the MIMO channel decays to a more serious extent or orthogonal characteristics are insufficient, the value of K has to be increased to enhance the receiving performance of the system. Consequently, the system complexity greatly increases by a multiple of (T−2)×M, thereby enlarging the area of the chip processor, increasing power consumption and reducing data processing amount.